OpenCV Q&A Forum - RSS feedhttp://answers.opencv.org/questions/OpenCV answersenCopyright <a href="http://www.opencv.org">OpenCV foundation</a>, 2012-2018.Mon, 23 Dec 2013 02:16:22 -0600Kalman filter: motion modelhttp://answers.opencv.org/question/25792/kalman-filter-motion-model/Hi all,
I'm using a Kalman filter to estimate the position of an object ( (x,y) coordinates ).
Regarding the state equations, I've seen the following approaches in different examples:
1) Auto regressive model:
--------------------------
x[t+1] = x[t] + V[t] + noise
where V[t] = (x[t] - x[t-1])
Thus: x[t+1] = x[t] + (x[t] - x[t-1]) + noise = 2x[t] - x[t-1] + noise
In this case, both x[t] and x[t-1] are kept in the state vector
2) Velocity estimation:
-----------------------
x[t+1] = x[t] + V[t] + noise
where V[t] = V[t-1] + noise
In this case, only x[t] and V[t] are kept in the state vector.
So my question is: is there really a difference between both approaches? Is any of them better than the other in certain situations?
Thanks a lot!
Kind regards,
ArnoArnokeMon, 23 Dec 2013 02:16:22 -0600http://answers.opencv.org/question/25792/